arXiv AI July 15, 2026 By Sidi Chang, Peiying Zhu, Yuxiao Chen, Rongdong Chai

Measurement Risk in Supervised Financial NLP: Rubric and Metric Sensitivity on JF-ICR

arXiv:2604. 27374v2 Announce Type: replace Abstract: As LLMs become credible readers of earnings calls, investor-relations Q\&A, guidance, and disclosure language, supervised financial NLP benchmarks increasingly function as decision evidence for model selection and deployment.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

2d ago

A Four-Axis Trustworthiness Benchmark for LLM-as-Judge in Principle-Based Regulation

arXiv:2608. 14329v1 Announce Type: cross Abstract: Principle-based regulation, with evaluative standards such as "fair, clear, and not misleading" or "deliver good outcomes", cannot be reduced to binary predicates, and LLM-as-judge is increasingly used as the substitute.

By Dipankar Sarkar

llms benchmarks safety

arXiv AI

Jul 31

Diagnosing Fine-Grained Inconsistency Classification in Financial Disclosure Text

arXiv:2607. 26368v1 Announce Type: cross Abstract: Financial disclosures contain numerical claims, temporal statements, entity references, policy commitments, and risk descriptions that may conflict in qualitatively different ways.

By Aman Kumar, Lasitha Vidyaratne, Dipanjan D Ghosh, Arnab Chakrabarti, Ahmed K Farahat

llms rag fine-tuning benchmarks

Hugging Face Trending Papers

Jul 29

Diagnosing Fine-Grained Inconsistency Classification in Financial Disclosure Text

Financial disclosures contain numerical claims, temporal statements, entity references, policy commitments, and risk descriptions that may conflict in qualitatively different ways. Detecting a conflict is only the first step: review workflows may also need to determine its type, since numerical, temporal, referential, factual, and normative inconsistencies require different evidence and downstream checks.

llms rag fine-tuning benchmarks

arXiv AI

Jun 12

Fin-RATE: A Real-world Financial Analytics and Tracking Evaluation Benchmark for LLMs on SEC Filings

arXiv:2602. 07294v4 Announce Type: replace-cross Abstract: With the increasing deployment of Large Language Models (LLMs) in the finance domain, LLMs are increasingly expected to parse complex regulatory disclosures.

By Yidong Jiang, Junrong Chen, Eftychia Makri, Jialin Chen, Peiwen Li, Ali Maatouk, Leandros Tassiulas, Eliot Brenner, Bing Xiang, Rex Ying

llms rag benchmarks safety

arXiv Machine Learning

Jul 17

How Much of a 10-K Matters? Aggregation-Dependent Value of Full-Text versus Risk-Factor Sentiment

arXiv:2607. 14174v1 Announce Type: new Abstract: Financial sentiment extraction has largely relied on news text and supervised extraction against return labels alone, leaving 10-K filings -- and volatility, the target risk disclosure is arguably best suited to informing -- comparatively unexplored.

By Sanggyu Sean Choi

arXiv AI

Jun 19

FFinRED: An Expert-Guided Benchmark Generation and Evaluation Framework for Financial LLM Red-Teaming

arXiv:2606. 19887v1 Announce Type: cross Abstract: Existing safety benchmarks target general adversarial scenarios but miss finance-specific risks.

By Chaeyun Kim, Daeyoung Park, Junghwan Kim, Jinyoung Jeong, Eunji Song, Yongtaek Lim, Minwoo Kim

llms benchmarks safety

Related stories

A Four-Axis Trustworthiness Benchmark for LLM-as-Judge in Principle-Based Regulation

Diagnosing Fine-Grained Inconsistency Classification in Financial Disclosure Text

Diagnosing Fine-Grained Inconsistency Classification in Financial Disclosure Text

Fin-RATE: A Real-world Financial Analytics and Tracking Evaluation Benchmark for LLMs on SEC Filings

How Much of a 10-K Matters? Aggregation-Dependent Value of Full-Text versus Risk-Factor Sentiment

FFinRED: An Expert-Guided Benchmark Generation and Evaluation Framework for Financial LLM Red-Teaming